Detection of microalgae objects based on the Improved YOLOv3 model,Environmental Science: Processes & Impacts

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Detection of microalgae objects based on the Improved YOLOv3 model
Environmental Science: Processes & Impacts ( IF 4.3 ) Pub Date : 2021-08-11 , DOI: 10.1039/d1em00159k
Mengying Cao _{1,

2} , Junsheng Wang _{1,

2} , Yantong Chen _{1,

2} , Yuezhu Wang _{1,

3}

Affiliation

Microalgae play a major role in the invasion of alien organisms with ballast water as a carrier, and traditional ballast water detection methods have many limitations in identifying microalgae species. Therefore, this paper proposes a method to identify microalgae in ballast water based on an Improved YOLOv3 model. The method first used a lightweight network MobileNet instead of the Darknet-53 network as the backbone network of feature extraction in the original YOLOv3 model. Secondly, improved spatial pyramid pooling (SPP) is introduced to pool and concatenate the multi-scale regional features so as to reduce the position error when detecting small objects. Then, by considering the overlap area of the bounding box, central point distance and aspect ratio, the Complete IoU (CIoU) algorithm is used to optimize the loss function of the YOLOv3 model. Finally, the proposed method is experimentally compared with other latest methods on the established dataset. The experimental results demonstrated that under the same conditions, this Improved YOLOv3 model achieves an average accuracy of 98.90%, and the detection efficiency is 8.59% higher than that of the original YOLOv3 model and is better than the existing methods. The average time of this method to identify a single image is 0.086 s, and it has a good detection effect on the identification of microalgae species.

中文翻译：

基于改进YOLOv3模型的微藻目标检测

微藻在以压载水为载体的外来生物入侵中起主要作用，而传统的压载水检测方法在识别微藻种类方面存在诸多局限性。因此，本文基于改进的YOLOv3模型提出了一种识别压载水中微藻的方法。该方法首先使用轻量级网络MobileNet代替Darknet-53网络作为原始YOLOv3模型中特征提取的骨干网络。其次，引入改进的空间金字塔池化（SPP）来池化和串联多尺度区域特征，以减少检测小物体时的位置误差。然后，通过考虑bounding box的重叠面积、中心点距离和纵横比，使用Complete IoU（CIoU）算法优化YOLOv3模型的损失函数。最后，在已建立的数据集上将所提出的方法与其他最新方法进行实验比较。实验结果表明，在相同条件下，改进后的YOLOv3模型平均准确率达到98.90%，检测效率比原YOLOv3模型提高8.59%，优于现有方法。该方法识别单幅图像的平均时间为0.086 s，对微藻种类的识别具有良好的检测效果。比原始 YOLOv3 模型高 59%，并且优于现有方法。该方法识别单幅图像的平均时间为0.086 s，对微藻种类的识别具有良好的检测效果。比原始 YOLOv3 模型高 59%，并且优于现有方法。该方法识别单幅图像的平均时间为0.086 s，对微藻种类的识别具有良好的检测效果。

更新日期：2021-09-07

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